Kafka vs RabbitMQ: Why Use Kafka?
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A vital part of the successful completion of any project is the selection of the right tools.
When it comes to big data, sometimes the difficult choice here is picking the right streaming data service.
For example should you use Apache Kafka or RabbitMQ. Both platforms feature several functionalities and use cases that can help users make an informed decision.
Apache Kafka and RabbitMQ are two top platforms in the area of messaging services. Although both platforms handle messaging differently, the difference lies in their selected architecture, design, and approach to delivery.
But what is Apache Kafka and RabbitMQ?
Apache Kafka and RabbitMQ are open-source platforms with pub/sub(which we will describe later) systems that are commercial -supported and used by several enterprises.
What is Apache Kafka?
Apache Kafka, in simple terms, is a message bus optimized for high-access data replays and streams. The robust message broker allows applications to continually process and re-process stream data. The open-source platform uses an uncomplicated and easy routing approach that engages routing key in sending messages to a topic. Launched in 2011, the Kafka tool was created for streaming set-ups.
What is RabbitMQ?
RabbitMQ is an all-round messaging broker with supports protocols. This includes Advanced Message Queuing Protocol (AMQP), MQ Telemetry Transport (MQTT), and Simple (or Streaming) text-oriented Messaging Protocol (STOMP).
RabbitMQ can handle use cases seeking high efficiency. For example RabbitMQ is great for processing of online payments. RabbitMQ can also serve as a message broker amongst microservices. Launched in 2007, RabbitMQ started as a primary element in messaging and SOA systems. Nowadays, its expanded roles also include streaming use cases.
So, if you are considering whether to use Apache Kafka or RabbitMQ, read on to learn about the difference in architectures, approaches, and their performance pros and cons.
Architecture Differences
Apache Kafka Architecture
The Apache Kafka Architecture uses a high volume of publish-subscribe messages and streams platform that is quick and scalable. The robust message store, like logs, makes use of server clusters which holds several records in topics (categories).
All Kafka Messages feature a key, value, and timestamp. The smart consumer or dumb broker model do not attempt tracking on consumer messages and only retains unread messages. Apache Kafka holds all messages for a defined time frame.
Global Apache Kafka architecture (Source)
RabbitMQ Architecture
The RabbitMQ Architecture makes use of an all-round message broker which entails variations in point to point, request/reply, and pub/sub communication designs. The use of dumb consumer and smart broker method allows for reliable message delivery to consumers with similar speed to that of broker monitoring the state of consumers.
With the use of synchronous or asynchronous communication methods, the platform provides adequate support for several plugins including .NET, client libraries, Java, node.js, Ruby, and more. It also makes available distributed deployment scenarios alongside multi-node cluster to cluster federation with zero reliance on external services.
With RabbitMQ, publishers can transmit messages to exchanges, and retrieving messages from queues by consumers. The decoupling producers from lines through exchanges guarantee that producers are not troubled with hardcoded routing choices.
RabbitMQ architecture (Source)
Publish/ Subscribe (or Pub/Sub)
Publish/ Subscribe is amongst the main messaging patterns for asynchronous messaging, a messaging scheme where message production is decoupled from its processing by a consumer.
Apache Kafka
In Apache Kafka, the platform is created for high volume publish-subscribe messages and streams, which are intended to be durable, quick, and scalable. In essence, Kafka makes available a sustainable message store and a server cluster.
RabbitMQ
In RabbitMQ, the design entails an all-round message broker, using several variations of point to point, request/reply and pub-sub communication styles patterns.
Push/ Pull Model
Apache Kafka: Pull-based method
Kafka makes use of a pull model where consumers make message requests in batches from a specified offset. Apache Kafka also allows for long-pooling, which stops tight loops when no message goes through the offset.
The pull model remains logical for Apache Kafka due to its partitions. Its platform makes available message order within a barrier without contending consumers. This approach lets users leverage message batching for efficient message delivery and better throughput.
RabbitMQ: Push-based method
RabbitMQ pushes messages to the consumer, which includes a prefetch limit configuration essential to preventing the consumer from becoming overwhelmed by multiple messages. They can also be useful for low latency messaging. The purpose of the push method is towards the quick distribution of individual messages individually, alongside a guarantee that all of it is parallelized evenly and messages are processed in an ordered queue, usually as they arrive.
Use Cases
Apache Kafka
Apache Kafka provides an additional broker itself, which it is best known and a popular element of the platform.
The additional broker has been premeditated and marketed in the direction of stream processing set-ups. Also, the addition of Kafka Streams serves as an alternative to streaming platforms like Apache Flink, Apache Spark, Google Cloud Data Flow, and Spring Cloud Data Flow.
The exceptional use cases documentation provides a detailed use case for Apache Kafka including its Commit logs, Event Sourcing, Log Aggregation, Metrics, Webs Activity Tracking, and more tasks.
RabbitMQ
RabbitMQ delivers an all-round messaging solution with widespread usage amongst web servers’ timely response to requests in place of enforced response to performing resource-heavy measures while users await the result. RabbitMQ is also excellent for distributing messages to multiple receivers as it offers a lot of features for reliable delivery, federation, management tools, routing, security, and other functions.
With assistance from additional software, RabbitMQ can also effectively address several substantial uses cases. Overall, picking the right tool is a key part of development depending on the overall technical environment.
Conclusion
Both Apache Kafka and RabbitMQ platforms offer a variety of critical services intended to suit a lot of the demands. RabbitMQ is sufficient for simple use cases that entail low data traffic. Also, with RabbitMQ, other additional benefits include flexible routing prospects and priority queue options. On the other hand, if the proposed use case features the need for massive data and high traffic, then Apache Kafka is one worth considering.
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